Three-Dimensional Variational Data Assimilation with Rapid Update Cycling for Short-Range Precipitation Forecasting: A Case Study of Heavy Rainfall in Bali, Indonesia

TL;DR

This paper demonstrates that high-frequency 3D-Var data assimilation with rapid update cycling significantly improves short-range precipitation forecasts in tropical maritime environments, especially during heavy rainfall events like in Bali.

Contribution

It introduces a novel application of 3D-Var data assimilation with rapid update cycles in a tropical setting, showing substantial forecast accuracy improvements.

Findings

1-hour cycling yields 75% reduction in temperature RMSE.

1-hour cycling reduces precipitation RMSE by 57%.

Frequent cycling improves mesoscale convective evolution capture.

Abstract

This study evaluates the effectiveness of three-dimensional variational (3D-Var) data assimilation coupled with a Rapid Update Cycle (RUC) framework for improving short-range precipitation forecasts over the Indonesian Maritime Continent (IMC). We employ the Weather Research and Forecasting (WRF) model and its data assimilation component (WRFDA) to assimilate surface observations from Automatic Weather Stations (AWS) at cycling intervals of 1, 3, 6, and 12 hours. Our test case is a heavy rainfall event on 7 July 2023 in Bali Province, during which accumulated precipitation exceeded 193 mm.day$^{-1}$. The 1-hour cycling interval yields the lowest root-mean-square error (RMSE) for both 2-meter temperature (0.0-0.3$\,^\circ$C) and hourly precipitation (1.295 mm.h$^{-1}$), corresponding to reductions of roughly 75% and 57%, respectively, relative to non-assimilated forecasts. Frequent cycling constrains initial-condition errors and captures mesoscale convective evolution, as confirmed by improved spatial agreement with radar reflectivity observations. These results demonstrate that high-frequency assimilation cycling offers clear advantages for nowcasting in tropical maritime environments.